ManukaAnalysis

F1 Layout

Map indicates the layout of the F1 generation resulting from a cross between EC201 and EC103 parents. Column 1 is approximately lengthways facing north.

“Map of F1 Trees”

Height Diagram

Diagram indicates the areas of leaf collection regarding height. Each Tree generally had 10 leaves collected (some trees have been sampled more than once), leaves were selected from low, mid and high points of the tree. The first leaf sampled from each tree was measured twice for replication comparison.

“Diagram of leaf collection levels”

Import and Arrange Data

“Full.xlsx”, sheet “Full” contains measurement information from sampling with the Dualex (https://www.force-a.com/en/capteurs-optiques-optical-sensors/dualex-scientific-chlorophyll-meter/), including;

Surface content of chlorophyll in \(g/cm^2\) (Chl)
Epidermal Flavoid content in absorbance units; Flavonol(Flav) and Anthocyanin(Anth)
Nitrogen Balance Index status is calculated using Chlorophyll and Flavonol values automatically (NBI)

It also contains information about the block position, the leaf height information, and presense or absence of flowering

Sheet “Dup” contains only the replicated samples

# Import Data Measures
Data <-read.xlsx("Full.xlsx", sheetName ="Full")
head(Data)

##   Collection.Day Allocation Block Column Row group Group.ID Tree.ID Rep.
## 1              4         CG     0      0   0    20       OG      CG    N
## 2              4         CG     0      0   0    20       OG      CG    N
## 3              4         CG     0      0   0    20       OG      CG    N
## 4              4         CG     0      0   0    20       OG      CG    N
## 5              4         CG     0      0   0    20       OG      CG    N
## 6              4         CG     0      0   0    20       OG      CG    N
##   measure Height Flower    Chl  Flav  Anth   NBI
## 1       9      H      Y 28.220 2.418 0.205 11.67
## 2       3      L      Y 27.958 2.298 0.691 12.17
## 3      11      H      Y 33.727 2.458 0.527 13.72
## 4       4      L      Y 25.938 1.758 0.172 14.76
## 5      10      H      Y 36.205 2.283 0.270 15.86
## 6       6      M      Y 34.332 2.115 0.150 16.23

Data$Column = as.factor(Data$Column)
Data$Row = as.factor(Data$Row)

# Import Replicate Data
Dup <-read.xlsx("Full.xlsx", sheetName ="Dup")
head(Dup)

##   Collection.Day group Group.ID Tree.ID Rep. measure Height   Chl  Flav  Anth
## 1            2.0    23       60   IN4DV   Y1       1      L 1.916 2.363 0.174
## 2            1.5     3        1   IN4BT   Y1       1      L 3.124 2.300 0.191
## 3            2.0    17       54   IN4DL   Y2       2      L 3.414 1.826 0.112
## 4            1.5    32       28   IN4CP   Y2       2      L 4.097 1.943 0.051
## 5            2.0     9       46   IN4DC   Y1       1      L 4.909 1.928 0.103
## 6            1.5     5        3   IN4BW   Y1       1      L 4.924 1.848 0.165
##    NBI
## 1 0.81
## 2 1.36
## 3 1.87
## 4 2.11
## 5 2.55
## 6 2.66

#Isolate Crimson Glory Outgroup
CG = Data[c(1:11),]

#Isolate East Cape 201 Parent
EC201 = Data[c(12:21),]

#Isolate East Cape 103 Parent
EC103 = Data[c(22:33),]

#Isolate Offspring from the Parental Cross
F1 = Data[c(34:1825),]

Replicate Analysis

Replicates were taken by re-measuring a single leaf sample taken from each tree, this was to help establish the consistency of measurements given by the Dualex to help verify measurement accuracy. Replicates are split into two groups each containing one replicate measure from each leaf sample.

Replicate Data Overview

## Warning in RepAnth$SD = sd(Dup$Anth): Coercing LHS to a list

## Warning in RepChl$SD = sd(Dup$Chl): Coercing LHS to a list

## Warning in RepFlav$SD = sd(Dup$Flav): Coercing LHS to a list

## Warning in RepNBI$SD = sd(Dup$NBI): Coercing LHS to a list

##   Measure  Min. X1st.Qu.  Median        Mean X3rd.Qu.   Max.          SD
## 1    Anth 0.001  0.06500  0.0985  0.09799367  0.12525  0.254  0.04482991
## 2     Chl 1.916 25.82475 36.0270 35.76269937 46.61000 59.674 14.53891331
## 3    Flav 1.056  1.76775  1.9940  1.96720570  2.19725  2.714  0.28290856
## 4     NBI 0.810 12.70250 18.1900 18.73398734 24.45750 43.490  8.60482208

Replicate Group means

## # A tibble: 2 x 9
##   Rep.  Anth_mean Anth_sd Chl_mean Chl_sd Flav_mean Flav_sd NBI_mean NBI_sd
##   <fct>     <dbl>   <dbl>    <dbl>  <dbl>     <dbl>   <dbl>    <dbl>  <dbl>
## 1 Y1       0.0983  0.0454     34.9   14.6      1.98   0.286     18.2   8.55
## 2 Y2       0.0977  0.0444     36.6   14.5      1.95   0.280     19.2   8.66

Anthocyanin Replicate Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Chlorphyll Replicate Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Flavonol Replicate Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Nitrogen Balance Replicate Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

There is no statistically significant differences between the two groups of measurements, this is a good sign indicative of the accuracy of the Dualex.

ANOVA of Replicates

Anothcyanin

## Analysis of Variance Table
## 
## Response: Dup$Anth
##            Df  Sum Sq   Mean Sq F value Pr(>F)
## Dup$Rep.    1 0.00003 0.0000304  0.0151 0.9023
## Residuals 314 0.63303 0.0020160

Chlorophyll

## Analysis of Variance Table
## 
## Response: Dup$Chl
##            Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep.    1    218  218.33   1.033 0.3102
## Residuals 314  66366  211.36

Flavonol

## Analysis of Variance Table
## 
## Response: Dup$Flav
##            Df  Sum Sq  Mean Sq F value Pr(>F)
## Dup$Rep.    1  0.0521 0.052129  0.6506 0.4205
## Residuals 314 25.1596 0.080126

Nitrogen

## Analysis of Variance Table
## 
## Response: Dup$NBI
##            Df  Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep.    1    82.5  82.530   1.115 0.2918
## Residuals 314 23241.0  74.016

## Warning in rbind(R2, Replicate): number of columns of result is not a multiple
## of vector length (arg 1)

##         [,1]                  
## R2      "Rsquared"            
## AnthRep "4.80229034387657e-05"
## ChlRep  "0.00327902754725474" 
## FlavRep "0.00206765289606737" 
## NBIRep  "0.00353847674800645"

The absence of statistically significant results indicates that our replicates are likely to be consistent.

Allocation Analysis

Allocation refers to which group measurements were taken from, i.e. A Parental Tree (EC103 or EC201), Outgroup Tree (CG), Parental Offspring (F1)

Allocation Data Overview

##                Min.     1st Qu.      Median        Mean     3rd Qu.        Max.
## AllAnth  0.00100000  0.06800000  0.09600000  0.09772877  0.12400000  0.69100000
## AllChl   0.13000000 24.10200000 36.21000000 35.21443342 46.82300000 59.90500000
## AllFlav  1.05600000  1.80600000  1.97800000  1.96440493  2.12800000  2.86100000
## AllNBI   0.07000000 12.14000000 18.64000000 18.34807123 24.44000000 49.17000000

Allocation Group Means

## # A tibble: 4 x 5
##   Allocation   Anth   Chl  Flav   NBI
##   <fct>       <dbl> <dbl> <dbl> <dbl>
## 1 CG         0.258   36.0  2.17  16.7
## 2 EC103      0.0588  52.6  1.60  33.1
## 3 EC201      0.0779  52.6  1.71  31.7
## 4 F1         0.0971  35.0  1.97  18.2

Boxplots Comparing Mean, Median and Measurement Distributions of the Allocation Groups

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Allocation ANOVAs

Anthocyanin

## Analysis of Variance Table
## 
## Response: Data$Anth
##                   Df Sum Sq  Mean Sq F value    Pr(>F)    
## Data$Allocation    3 0.3040 0.101343  47.474 < 2.2e-16 ***
## Residuals       1821 3.8873 0.002135                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.07253727

Chlorophyll

## Analysis of Variance Table
## 
## Response: Data$Chl
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   6742 2247.21  9.9149 1.748e-06 ***
## Residuals       1821 412728  226.65                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.01607176

Flavonol

## Analysis of Variance Table
## 
## Response: Data$Flav
##                   Df  Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   2.713 0.90423  14.755 1.708e-09 ***
## Residuals       1821 111.598 0.06128                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.0237309

Nitrogen

## Analysis of Variance Table
## 
## Response: Data$Chl
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   6742 2247.21  9.9149 1.748e-06 ***
## Residuals       1821 412728  226.65                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.03353012

F1 Samples (Approx 3 years) appear more similar to that of the Crimson Glory plant than either/both parents - this is possibly due to age effects as CG is likely more similar in this regard being shorter (No age confirmed). ANOVAs incidate there is significant differences between the allocations - this is to be expected.

Parent Tree Analysis

Parent Tree Data Overview

##              Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
## ParAnth  0.004000  0.048250  0.058000  0.067500  0.086750  0.156000
## ParChl  30.722000 50.356750 55.739000 52.598909 57.794750 59.757000
## ParFlav  1.293000  1.475000  1.594000  1.650273  1.821000  2.138000
## ParNBI  14.370000 29.872500 34.025000 32.481818 35.892500 44.880000

Parent Tree Means

## # A tibble: 2 x 5
##   Tree.ID   Anth   Chl  Flav   NBI
##   <fct>    <dbl> <dbl> <dbl> <dbl>
## 1 EC103   0.0588  52.6  1.60  33.1
## 2 EC201   0.0779  52.6  1.71  31.7

Anthocyanin Parent Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Chlorophyll Parent Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Flavonol Parent Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Nitrogen Parent Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Parent t.tests

Anthocyanin

## 
##  Welch Two Sample t-test
## 
## data:  Parent$Anth by Parent$Tree.ID
## t = -1.2283, df = 19.107, p-value = 0.2342
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05154327  0.01340994
## sample estimates:
## mean in group EC103 mean in group EC201 
##          0.05883333          0.07790000

## [1] 0.07045294

Chlorophyll

## 
##  Welch Two Sample t-test
## 
## data:  Parent$Chl by Parent$Tree.ID
## t = -0.003842, df = 17.804, p-value = 0.997
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.830954  7.802387
## sample estimates:
## mean in group EC103 mean in group EC201 
##            52.59242            52.60670

## [1] 7.612864e-07

Flavonol

## 
##  Welch Two Sample t-test
## 
## data:  Parent$Flav by Parent$Tree.ID
## t = -1.141, df = 19.612, p-value = 0.2676
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.31409194  0.09215861
## sample estimates:
## mean in group EC103 mean in group EC201 
##            1.599833            1.710800

## [1] 0.06055719

## 
##  Welch Two Sample t-test
## 
## data:  Parent$NBI by Parent$Tree.ID
## t = 0.47573, df = 14.7, p-value = 0.6413
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.076667  7.987334
## sample estimates:
## mean in group EC103 mean in group EC201 
##            33.14333            31.68800

## [1] 0.01212803

Interesting sample pattern here, Chl and NBI start low and work high, Flav does the opposite. Maybe accuracy of measurements?

Parental Cross Analysis (F1 Generation)

A Brief Look at Some Tree Data

## # A tibble: 6 x 5
##   Tree.ID   Chl   NBI   Anth  Flav
##   <fct>   <dbl> <dbl>  <dbl> <dbl>
## 1 IN4BT    34.5  16.2 0.110   2.17
## 2 IN4BV    27.5  13.8 0.121   2.01
## 3 IN4BW    24.8  12.5 0.127   1.99
## 4 IN4BX    37.6  18.4 0.0905  2.05
## 5 IN4BY    24.2  12.5 0.110   1.98
## 6 IN4BZ    32.0  15.6 0.106   2.09

Anthocyanin Overview

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$Anth
##              Df Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Tree.ID  158 0.5253 0.0033247  1.8093 2.001e-08 ***
## Residuals  1633 3.0007 0.0018376                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.148978

Chlorophyll Overview

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$Chl
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## F1$Tree.ID  158  47130  298.29  1.3395 0.004474 **
## Residuals  1633 363634  222.68                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.1147362

Flavonol Overview

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$Flav
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Tree.ID  158 25.118 0.15897  3.0531 < 2.2e-16 ***
## Residuals  1633 85.030 0.05207                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.2280369

Nitrogen Overview

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$NBI
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Tree.ID  158  16378 103.661  1.5147 8.435e-05 ***
## Residuals  1633 111761  68.439                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.127818

A Closer Look at Invidual F1 Trees and Their Measurements

Msr = group_by(F1, Tree.ID, measure)
Msr = summarise(Msr, Anth = mean(Anth), Flav = mean(Flav), Chl = mean(Chl),NBI = mean(NBI))

#Select Random Column
#sample(1:4,10, replace = T)
#[1] 3 3 2 1 1 4 1 2 4 3

#Select Random Row
#sample(1:50,10, replace = T)
#[1] 34 37 44 20 44 47  9 19 22 40

IN4G5

IN4EP

IN4EY

IN4CD

IN4D8

IN4GV

IN4C2

IN4E3

IN4HK

IN4GC

F1 Height Analysis

## # A tibble: 3 x 5
##   Height   Anth   Chl  Flav   NBI
##   <fct>   <dbl> <dbl> <dbl> <dbl>
## 1 H      0.105   33.9  1.93  17.9
## 2 L      0.0968  35.6  1.98  18.5
## 3 M      0.0913  35.3  1.98  18.1

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Height Measure Distributions

Anthocyanin

Chlorophyll

Flavonol

Nitrogen

ANOVAS

Anthocyanin

## Analysis of Variance Table
## 
## Response: F1$Anth
##             Df Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Height    2 0.0526 0.0262759  13.533 1.468e-06 ***
## Residuals 1789 3.4735 0.0019416                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.01490394

Chlorophyll

## Analysis of Variance Table
## 
## Response: F1$Chl
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height    2    853  426.30  1.8605 0.1559
## Residuals 1789 409911  229.13

## [1] 0.002075652

Flavonol

## Analysis of Variance Table
## 
## Response: F1$Flav
##             Df  Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height    2   0.915 0.45758  7.4943 0.0005739 ***
## Residuals 1789 109.232 0.06106                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.008308538

## Analysis of Variance Table
## 
## Response: F1$NBI
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height    2    100  50.044  0.6992 0.4971
## Residuals 1789 128039  71.570

## [1] 0.0007810934

Height Analysis within Trees

## # A tibble: 6 x 6
## # Groups:   Height [1]
##   Height Tree.ID   Anth   Chl  Flav   NBI
##   <fct>  <fct>    <dbl> <dbl> <dbl> <dbl>
## 1 H      IN4BT   0.116   43.6  2.09 21.1 
## 2 H      IN4BV   0.104   28.1  1.93 14.7 
## 3 H      IN4BW   0.12    18.5  2.06  9.15
## 4 H      IN4BX   0.0875  43.2  2.13 20.3 
## 5 H      IN4BY   0.121   27.7  1.96 14.3 
## 6 H      IN4BZ   0.0817  39.1  2.10 18.5

Anthocyanin High vs Low

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: High$Anth
##            Df   Sum Sq   Mean Sq F value  Pr(>F)   
## Low$Anth    1 0.009644 0.0096440  10.431 0.00151 **
## Residuals 157 0.145162 0.0009246                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = High$Anth ~ Low$Anth)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.082724 -0.020196 -0.001244  0.020111  0.095669 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.075038   0.009659   7.769 9.67e-13 ***
## Low$Anth    0.312539   0.096773   3.230  0.00151 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03041 on 157 degrees of freedom
## Multiple R-squared:  0.0623, Adjusted R-squared:  0.05632 
## F-statistic: 10.43 on 1 and 157 DF,  p-value: 0.00151

Anthocyanin High vs Mid

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: High$Anth
##            Df   Sum Sq    Mean Sq F value Pr(>F)
## Mid$Anth    1 0.001607 0.00160739  1.6473 0.2012
## Residuals 157 0.153198 0.00097579

## 
## Call:
## lm(formula = High$Anth ~ Mid$Anth)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.080176 -0.019699 -0.001994  0.017792  0.120025 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.09193    0.01067   8.616 6.97e-15 ***
## Mid$Anth     0.14627    0.11397   1.283    0.201    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03124 on 157 degrees of freedom
## Multiple R-squared:  0.01038,    Adjusted R-squared:  0.00408 
## F-statistic: 1.647 on 1 and 157 DF,  p-value: 0.2012

Anthocyanin Mid vs Low

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: Mid$Anth
##            Df   Sum Sq    Mean Sq F value  Pr(>F)  
## Low$Anth    1 0.002597 0.00259684  5.6212 0.01896 *
## Residuals 157 0.072529 0.00046197                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = Mid$Anth ~ Low$Anth)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.074218 -0.012195 -0.000834  0.013593  0.058269 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.075378   0.006828  11.040   <2e-16 ***
## Low$Anth    0.162181   0.068404   2.371    0.019 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02149 on 157 degrees of freedom
## Multiple R-squared:  0.03457,    Adjusted R-squared:  0.02842 
## F-statistic: 5.621 on 1 and 157 DF,  p-value: 0.01896

Chlorophyll High vs Low

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: High$Chl
##            Df  Sum Sq Mean Sq F value Pr(>F)
## Low$Chl     1    40.6  40.577  0.4215 0.5171
## Residuals 157 15112.7  96.259

## 
## Call:
## lm(formula = High$Chl ~ Low$Chl)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24.4732  -6.5662   0.8979   6.6457  24.2522 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 36.03022    3.39455  10.614   <2e-16 ***
## Low$Chl     -0.05995    0.09234  -0.649    0.517    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.811 on 157 degrees of freedom
## Multiple R-squared:  0.002678,   Adjusted R-squared:  -0.003675 
## F-statistic: 0.4215 on 1 and 157 DF,  p-value: 0.5171

Chlorophyll High vs Mid

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: High$Chl
##            Df  Sum Sq Mean Sq F value Pr(>F)
## Mid$Chl     1     6.5   6.517  0.0676 0.7953
## Residuals 157 15146.8  96.476

## 
## Call:
## lm(formula = High$Chl ~ Mid$Chl)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24.3314  -6.5596   0.9516   7.1975  23.9071 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 34.75595    3.44048   10.10   <2e-16 ***
## Mid$Chl     -0.02468    0.09494   -0.26    0.795    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.822 on 157 degrees of freedom
## Multiple R-squared:  0.0004301,  Adjusted R-squared:  -0.005937 
## F-statistic: 0.06755 on 1 and 157 DF,  p-value: 0.7953

Chlorophyll Mid vs Low

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: Mid$Chl
##            Df  Sum Sq Mean Sq F value Pr(>F)
## Low$Chl     1    86.5  86.465  1.2786 0.2599
## Residuals 157 10617.2  67.625

## 
## Call:
## lm(formula = Mid$Chl ~ Low$Chl)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.2237  -4.2267   0.5812   5.1386  17.4487 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 32.16619    2.84521  11.305   <2e-16 ***
## Low$Chl      0.08752    0.07740   1.131     0.26    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.223 on 157 degrees of freedom
## Multiple R-squared:  0.008078,   Adjusted R-squared:  0.00176 
## F-statistic: 1.279 on 1 and 157 DF,  p-value: 0.2599

Flavonol High vs Low

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: High$Flav
##            Df  Sum Sq  Mean Sq F value   Pr(>F)   
## Low$Flav    1 0.15207 0.152067  7.9384 0.005462 **
## Residuals 157 3.00745 0.019156                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = High$Flav ~ Low$Flav)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.69197 -0.07612  0.01771  0.09061  0.31947 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.59267    0.11926  13.355  < 2e-16 ***
## Low$Flav     0.16916    0.06004   2.818  0.00546 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1384 on 157 degrees of freedom
## Multiple R-squared:  0.04813,    Adjusted R-squared:  0.04207 
## F-statistic: 7.938 on 1 and 157 DF,  p-value: 0.005462

Flavonol High vs Mid

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: High$Flav
##            Df  Sum Sq Mean Sq F value    Pr(>F)    
## Mid$Flav    1 0.36919 0.36919  20.773 1.034e-05 ***
## Residuals 157 2.79032 0.01777                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = High$Flav ~ Mid$Flav)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.69325 -0.06998  0.01107  0.09397  0.33382 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.30244    0.13749   9.473  < 2e-16 ***
## Mid$Flav     0.31517    0.06915   4.558 1.03e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1333 on 157 degrees of freedom
## Multiple R-squared:  0.1169, Adjusted R-squared:  0.1112 
## F-statistic: 20.77 on 1 and 157 DF,  p-value: 1.034e-05

Flavonol Mid vs Low

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: Mid$Flav
##            Df Sum Sq Mean Sq F value    Pr(>F)    
## Low$Flav    1 0.5438 0.54376  26.906 6.506e-07 ***
## Residuals 157 3.1729 0.02021                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = Mid$Flav ~ Low$Flav)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.42522 -0.08825  0.00511  0.08340  0.47765 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.34975    0.12249  11.019  < 2e-16 ***
## Low$Flav     0.31987    0.06167   5.187 6.51e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1422 on 157 degrees of freedom
## Multiple R-squared:  0.1463, Adjusted R-squared:  0.1409 
## F-statistic: 26.91 on 1 and 157 DF,  p-value: 6.506e-07

NBI High vs Low

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: High$NBI
##            Df Sum Sq Mean Sq F value Pr(>F)
## Low$NBI     1   12.2  12.156  0.4216 0.5171
## Residuals 157 4527.1  28.835

## 
## Call:
## lm(formula = High$NBI ~ Low$NBI)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.3802  -3.4352  -0.0889   3.7378  11.3144 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 18.88822    1.61024  11.730   <2e-16 ***
## Low$NBI     -0.05431    0.08365  -0.649    0.517    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.37 on 157 degrees of freedom
## Multiple R-squared:  0.002678,   Adjusted R-squared:  -0.003674 
## F-statistic: 0.4216 on 1 and 157 DF,  p-value: 0.5171

NBI High vs Mid

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: High$NBI
##            Df Sum Sq Mean Sq F value Pr(>F)
## Mid$NBI     1    5.6  5.6094  0.1943   0.66
## Residuals 157 4533.7 28.8769

## 
## Call:
## lm(formula = High$NBI ~ Mid$NBI)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.1320  -3.5765   0.0304   4.0196  11.0499 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 18.61881    1.72974  10.764   <2e-16 ***
## Mid$NBI     -0.04062    0.09217  -0.441     0.66    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.374 on 157 degrees of freedom
## Multiple R-squared:  0.001236,   Adjusted R-squared:  -0.005126 
## F-statistic: 0.1943 on 1 and 157 DF,  p-value: 0.66

NBI Mid vs Low

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: Mid$NBI
##            Df Sum Sq Mean Sq F value  Pr(>F)  
## Low$NBI     1  106.0 105.990  5.0525 0.02598 *
## Residuals 157 3293.5  20.978                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = Mid$NBI ~ Low$NBI)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.0884  -2.6169  -0.0763   2.5932  11.7726 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.21189    1.37343  11.076   <2e-16 ***
## Low$NBI      0.16038    0.07135   2.248    0.026 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.58 on 157 degrees of freedom
## Multiple R-squared:  0.03118,    Adjusted R-squared:  0.02501 
## F-statistic: 5.053 on 1 and 157 DF,  p-value: 0.02598

Row Analysis

Sample of Row Means

## # A tibble: 6 x 5
##   Row     Chl   NBI   Anth  Flav
##   <fct> <dbl> <dbl>  <dbl> <dbl>
## 1 1      35.6  17.3 0.107   2.09
## 2 10     30.5  15.7 0.112   1.98
## 3 11     29.9  15.0 0.112   2.04
## 4 12     34.6  18.7 0.0968  1.84
## 5 13     31.3  17.5 0.107   1.86
## 6 14     35.0  17.9 0.0888  1.98

Investigating Differences Between Rows with 2,3 and 4 Trees.

Summary of Rows with 2 Trees

##       Row         Chl             NBI             Anth             Flav      
##  17     :1   Min.   :32.13   Min.   :17.14   Min.   :0.1163   Min.   :1.887  
##  0      :0   1st Qu.:32.13   1st Qu.:17.14   1st Qu.:0.1163   1st Qu.:1.887  
##  1      :0   Median :32.13   Median :17.14   Median :0.1163   Median :1.887  
##  10     :0   Mean   :32.13   Mean   :17.14   Mean   :0.1163   Mean   :1.887  
##  11     :0   3rd Qu.:32.13   3rd Qu.:17.14   3rd Qu.:0.1163   3rd Qu.:1.887  
##  12     :0   Max.   :32.13   Max.   :17.14   Max.   :0.1163   Max.   :1.887  
##  (Other):0

Summary of Rows with 3 Trees

##       Row          Chl             NBI             Anth              Flav      
##  1      : 1   Min.   :29.19   Min.   :14.38   Min.   :0.07868   Min.   :1.820  
##  10     : 1   1st Qu.:32.02   1st Qu.:16.44   1st Qu.:0.08535   1st Qu.:1.931  
##  11     : 1   Median :34.38   Median :17.81   Median :0.09354   Median :1.974  
##  14     : 1   Mean   :34.97   Mean   :18.12   Mean   :0.09677   Mean   :1.973  
##  15     : 1   3rd Qu.:37.05   3rd Qu.:19.46   3rd Qu.:0.10917   3rd Qu.:2.037  
##  16     : 1   Max.   :43.89   Max.   :23.16   Max.   :0.12239   Max.   :2.092  
##  (Other):33

Summary of Rows with 4 Trees

##       Row         Chl             NBI             Anth              Flav      
##  12     :1   Min.   :31.33   Min.   :16.99   Min.   :0.08183   Min.   :1.839  
##  13     :1   1st Qu.:34.20   1st Qu.:17.74   1st Qu.:0.09326   1st Qu.:1.898  
##  23     :1   Median :35.08   Median :18.29   Median :0.09747   Median :1.944  
##  32     :1   Mean   :35.09   Mean   :18.48   Mean   :0.09702   Mean   :1.941  
##  41     :1   3rd Qu.:36.36   3rd Qu.:19.10   3rd Qu.:0.10265   3rd Qu.:1.995  
##  45     :1   Max.   :38.42   Max.   :20.71   Max.   :0.10721   Max.   :2.032  
##  (Other):4

Looking at Row Differences in Variance

## [1] 0.0001562666

## [1] 6.039853e-05

## [1] 14.76679

## [1] 4.10476

## [1] 0.004848629

## [1] 0.004125519

## [1] 5.602987

## [1] 1.155704

T.Tests to Compare Similarity Between Rows with 3 and Rows with 4 Trees.

## 
##  Welch Two Sample t-test
## 
## data:  R4$Anth and R3$Anth
## t = 0.081174, df = 22.551, p-value = 0.936
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.006306855  0.006821440
## sample estimates:
##  mean of x  mean of y 
## 0.09702315 0.09676586

## 
##  Welch Two Sample t-test
## 
## data:  R4$Chl and R3$Chl
## t = 0.13309, df = 27.683, p-value = 0.8951
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.702350  1.938809
## sample estimates:
## mean of x mean of y 
##  35.08876  34.97053

## 
##  Welch Two Sample t-test
## 
## data:  R4$Flav and R3$Flav
## t = -1.3877, df = 14.921, p-value = 0.1856
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08156262  0.01725693
## sample estimates:
## mean of x mean of y 
##  1.941185  1.973338

## 
##  Welch Two Sample t-test
## 
## data:  R4$Flav and R3$Flav
## t = -1.3877, df = 14.921, p-value = 0.1856
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08156262  0.01725693
## sample estimates:
## mean of x mean of y 
##  1.941185  1.973338

There appears to be no signficant differences between Rows with 3 trees and rows with 4 trees for any of the measures.

Row Plots and ANOVAs

Anthocyanin

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Chlorophyll

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Flavonol

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Nitrogen

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Row ANOVA

## Analysis of Variance Table
## 
## Response: F1$Anth
##             Df Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Row      49 0.2346 0.0047878  2.5339 4.261e-08 ***
## Residuals 1742 3.2914 0.0018895                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.06653399

## Analysis of Variance Table
## 
## Response: F1$Chl
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Row      49  21394  436.61  1.9533 0.0001046 ***
## Residuals 1742 389370  223.52                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.0520826

## Analysis of Variance Table
## 
## Response: F1$Flav
##             Df  Sum Sq  Mean Sq F value   Pr(>F)    
## F1$Row      49   8.215 0.167646   2.865 3.11e-10 ***
## Residuals 1742 101.933 0.058515                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.07457888

## Analysis of Variance Table
## 
## Response: F1$NBI
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## F1$Row      49   7866 160.527   2.325 8.14e-07 ***
## Residuals 1742 120273  69.043                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.0613852

Column Analysis

Column Means

## # A tibble: 4 x 5
##   Column   Chl   NBI   Anth  Flav
##   <fct>  <dbl> <dbl>  <dbl> <dbl>
## 1 1       33.8  17.2 0.0945  2.01
## 2 2       35.4  18.6 0.0992  1.94
## 3 3       36.0  18.7 0.0990  1.96
## 4 4       34.3  18.7 0.0912  1.88

Data Overview of Column

##   0   1   2   3   4 
##   0 592 556 539 105

Column Boxplots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

ANOVA

## Analysis of Variance Table
## 
## Response: F1$Anth
##             Df Sum Sq   Mean Sq F value Pr(>F)
## F1$Column    3 0.0122 0.0040655  2.0687 0.1024
## Residuals 1788 3.5138 0.0019652

## [1] 0.003458982

## Analysis of Variance Table
## 
## Response: F1$Chl
##             Df Sum Sq Mean Sq F value  Pr(>F)  
## F1$Column    3   1586  528.66  2.3101 0.07453 .
## Residuals 1788 409178  228.85                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.003861029

## Analysis of Variance Table
## 
## Response: F1$Flav
##             Df  Sum Sq Mean Sq F value    Pr(>F)    
## F1$Column    3   2.305 0.76823  12.737 3.087e-08 ***
## Residuals 1788 107.843 0.06031                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.02092362

## Analysis of Variance Table
## 
## Response: F1$NBI
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## F1$Column    3    821 273.626  3.8427 0.009333 **
## Residuals 1788 127318  71.207                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.006406153

## Analysis of Variance Table
## 
## Response: F1$Anth
##                              Df  Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Height                     2 0.05255 0.0262759 14.9537 3.788e-07 ***
## F1$Column                     3 0.01228 0.0040942  2.3300 0.0727244 .  
## F1$Row                       49 0.23127 0.0047199  2.6861 6.175e-09 ***
## F1$Height:F1$Column           6 0.02055 0.0034247  1.9490 0.0699958 .  
## F1$Height:F1$Row             98 0.20024 0.0020433  1.1628 0.1394606    
## F1$Column:F1$Row            106 0.28473 0.0026861  1.5287 0.0006952 ***
## F1$Height:F1$Column:F1$Row  212 0.41375 0.0019517  1.1107 0.1490621    
## Residuals                  1315 2.31066 0.0017572                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.3446865

## Analysis of Variance Table
## 
## Response: F1$Anth
##                    Df  Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Height           2 0.05255 0.0262759 14.2135 7.586e-07 ***
## F1$Row             49 0.23404 0.0047763  2.5837 2.178e-08 ***
## F1$Height:F1$Row   98 0.20394 0.0020810  1.1257    0.1942    
## Residuals        1642 3.03551 0.0018487                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.1391174

## Analysis of Variance Table
## 
## Response: F1$Chl
##                              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height                     2    853  426.30  2.0422  0.130152    
## F1$Column                     3   1577  525.57  2.5178  0.056677 .  
## F1$Row                       49  21813  445.16  2.1326 1.244e-05 ***
## F1$Height:F1$Column           6   1282  213.61  1.0233  0.408244    
## F1$Height:F1$Row             98  27033  275.85  1.3215  0.022708 *  
## F1$Column:F1$Row            106  24037  226.77  1.0863  0.265386    
## F1$Height:F1$Column:F1$Row  212  59670  281.46  1.3484  0.001427 ** 
## Residuals                  1315 274499  208.74                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.3317345

## Analysis of Variance Table
## 
## Response: F1$Chl
##                    Df Sum Sq Mean Sq F value   Pr(>F)    
## F1$Height           2    853  426.30  1.9372  0.14444    
## F1$Row             49  21279  434.27  1.9734 8.34e-05 ***
## F1$Height:F1$Row   98  27286  278.43  1.2652  0.04464 *  
## Residuals        1642 361346  220.06                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.1203081

## Analysis of Variance Table
## 
## Response: F1$Chl
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Row      49  21394  436.61  1.9533 0.0001046 ***
## Residuals 1742 389370  223.52                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.0520826

## Analysis of Variance Table
## 
## Response: F1$Flav
##                              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height                     2  0.915 0.45758  9.3660 9.143e-05 ***
## F1$Column                     3  2.323 0.77447 15.8523 3.957e-10 ***
## F1$Row                       49  8.139 0.16611  3.4001 1.213e-13 ***
## F1$Height:F1$Column           6  1.004 0.16728  3.4240  0.002334 ** 
## F1$Height:F1$Row             98  5.743 0.05860  1.1995  0.096365 .  
## F1$Column:F1$Row            106 14.790 0.13953  2.8559 < 2.2e-16 ***
## F1$Height:F1$Column:F1$Row  212 12.988 0.06126  1.2540  0.012358 *  
## Residuals                  1315 64.245 0.04886                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.4167369

## Analysis of Variance Table
## 
## Response: F1$NBI
##                              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height                     2    100  50.044  0.7850 0.4563286    
## F1$Column                     3    818 272.802  4.2793 0.0051365 ** 
## F1$Row                       49   7816 159.511  2.5021 8.551e-08 ***
## F1$Height:F1$Column           6    792 132.004  2.0707 0.0539220 .  
## F1$Height:F1$Row             98   8175  83.418  1.3085 0.0268723 *  
## F1$Column:F1$Row            106   7765  73.250  1.1490 0.1510179    
## F1$Height:F1$Column:F1$Row  212  18842  88.877  1.3942 0.0004396 ***
## Residuals                  1315  83831  63.750                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.3457807

## Analysis of Variance Table
## 
## Response: F1$NBI
##                    Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Row             49   7866 160.527  2.3456 6.383e-07 ***
## F1$Column           3    789 263.021  3.8432   0.00934 ** 
## F1$Row:F1$Column  106   7724  72.864  1.0647   0.31303    
## Residuals        1633 111761  68.439                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.127818

Controlling for Height,Column,Row effects

Dat = F1

#Identifying outliers outside 
OBA <-boxplot(Dat$Anth,range = 3.5, plot = F)$out
OBC <-boxplot(Dat$Chl,range = 3, plot = F)$out
OBF <-boxplot(Dat$Flav,range = 3, plot = F)$out
OBN <-boxplot(Dat$NBI,range = 3,plot=F)$out

#Isolating outliers exceeding
OA = Dat[which(Dat$Anth %in% OBA),];OA

##     Collection.Day Allocation Block Column Row group Group.ID Tree.ID Rep.
## 605              3         F1    34      3  17    38      116   IN4FM    N
## 694              2         F1    10      2  20    33       69   IN4E4    N
##     measure Height Flower    Chl  Flav  Anth   NBI
## 605      11      H      N 59.122 1.640 0.483 36.05
## 694       7      M      N 18.511 1.401 0.370 13.21

OC = Dat[which(Dat$Anth %in% OBC),];OC

##  [1] Collection.Day Allocation     Block          Column         Row           
##  [6] group          Group.ID       Tree.ID        Rep.           measure       
## [11] Height         Flower         Chl            Flav           Anth          
## [16] NBI           
## <0 rows> (or 0-length row.names)

OF = Dat[which(Dat$Anth %in% OBF),];OF

##  [1] Collection.Day Allocation     Block          Column         Row           
##  [6] group          Group.ID       Tree.ID        Rep.           measure       
## [11] Height         Flower         Chl            Flav           Anth          
## [16] NBI           
## <0 rows> (or 0-length row.names)

ON = Dat[which(Dat$Anth %in% OBN),];ON

##  [1] Collection.Day Allocation     Block          Column         Row           
##  [6] group          Group.ID       Tree.ID        Rep.           measure       
## [11] Height         Flower         Chl            Flav           Anth          
## [16] NBI           
## <0 rows> (or 0-length row.names)

Dat <- Dat[-which(Dat$Anth %in% OBA),]

DATG <- ggplot(F1) + aes(x = Tree.ID, y = Anth,) + geom_boxplot(fill = "dodgerblue1", size = 1.25) + ylab("Anth") + xlab("Tree ID") + ggtitle("Anthocyanin Raw Data") +stat_summary(fun.y=mean, colour="gray90", geom="point", shape=18, size=3) +theme_bw()+theme(plot.title = element_text(hjust = 0.5))+geom_hline(yintercept = mean(F1$Anth))

## Warning: `fun.y` is deprecated. Use `fun` instead.

DATG2 <- ggplot(Dat) + aes(x = Tree.ID, y = Anth,) + geom_boxplot(fill = "dodgerblue1", size = 1.25) + ylab("Anth") + xlab("Tree ID") + ggtitle("Anthocyanin Outliers Removed") +stat_summary(fun.y=mean, colour="gray90", geom="point", shape=18, size=3) +theme_bw()+theme(plot.title = element_text(hjust = 0.5))+geom_hline(yintercept = mean(Dat$Anth))

## Warning: `fun.y` is deprecated. Use `fun` instead.

grid.arrange(DATG,DATG2)

#Total Mean
Dat$ATmean = mean(Dat$Anth)
Dat$CTmean = mean(Dat$Chl)
Dat$FTmean = mean(Dat$Flav)
Dat$NTmean = mean(Dat$NBI)

#Remove Total Mean
Dat$Anth2 = Dat$Anth - Dat$ATmean
Dat$Chl2 = Dat$Chl - Dat$CTmean
Dat$Flav2 = Dat$Flav - Dat$FTmean
Dat$NBI2 = Dat$NBI - Dat$NTmean

#Height Mean for Anth and Flav
DatH = group_by(Dat, Height)
DatH = summarise(DatH, HAnth = mean(Anth, na.rm = T), HChl = mean(Chl, na.rm =T), HFlav = mean(Flav,na.rm = T), HNBI = mean(NBI, na.rm = T))

#Add Height Mean to Data
Dat =merge(Dat, DatH, by.x = "Height")

#Calculate Height Mean Deviation from Total Mean 
Dat$ATH = Dat$ATmean - Dat$HAnth
Dat$CTH = Dat$CTmean - Dat$HChl
Dat$FTH = Dat$FTmean - Dat$HFlav
Dat$NTH = Dat$NTmean - Dat$HNBI

#Controlling for Height Anth and Flav
Dat$Anth3 = Dat$Anth2 + Dat$ATH
Dat$Chl3 = Dat$Chl2 + Dat$CTH
Dat$Flav3 = Dat$Flav2 + Dat$FTH
Dat$NBI3 = Dat$NBI2 + Dat$NTH

#Column Mean for Chl, Flav,NBI
DatC = group_by(Dat, Column)
DatC = summarise(DatC, CAnth = mean(Anth, na.rm = T), CChl = mean(Chl, na.rm = T) ,CFlav = mean(Flav,na.rm = T), CNBI = mean(NBI,na.rm = T))

#Add Column Mean to Data
Dat =merge(Dat, DatC, by.x = "Column")

#Calculate Column Mean Deviation from Total Mean
Dat$ATC = Dat$ATmean - Dat$CAnth
Dat$CTC = Dat$CTmean - Dat$CChl
Dat$FTC = Dat$FTmean - Dat$CFlav
Dat$NTC = Dat$NTmean - Dat$CNBI

#Controlling for Column Chl, Flav and NBI
Dat$Anth4 = Dat$Anth3 + Dat$ATC
Dat$Chl4 = Dat$Chl2 + Dat$CTC
Dat$Flav4 = Dat$Flav3 + Dat$FTC
Dat$NBI4 = Dat$NBI3 + Dat$NTC

#Row Mean for Anth, Chl, Flav and and NBI
DatR = group_by(Dat, Row)
DatR = summarise(DatR, RAnth = mean(Anth,na.rm = T),RChl = mean(Chl,na.rm = T), RFlav = mean(Flav,na.rm = T), RNBI = mean(NBI,na.rm = T))

#Add Row Mean to Data
Dat =merge(Dat, DatR, by.x = "Row")

#Calculate Row Mean Deviation from Total Mean 
Dat$ATR = Dat$ATmean - Dat$RAnth
Dat$CTR = Dat$CTmean - Dat$RChl
Dat$FTR = Dat$FTmean - Dat$RFlav
Dat$NTR = Dat$NTmean - Dat$RNBI

#Controlling for Row in Anth, Chl, Flav and NBI
Dat$Anth5 = Dat$Anth4 + Dat$ATR
Dat$Chl5 = Dat$Chl4 + Dat$CTR
Dat$Flav5 = Dat$Flav4 + Dat$FTR
Dat$NBI5 = Dat$NBI4 + Dat$NTR

New ANOVAs

## Analysis of Variance Table
## 
## Response: Dat$Anth5
##                                 Df  Sum Sq   Mean Sq F value  Pr(>F)    
## Dat$Height                       2 0.00000 0.0000013  0.0008 0.99918    
## Dat$Column                       3 0.00043 0.0001418  0.0869 0.96722    
## Dat$Row                         49 0.00058 0.0000118  0.0072 1.00000    
## Dat$Height:Dat$Column            6 0.01972 0.0032863  2.0144 0.06088 .  
## Dat$Height:Dat$Row              98 0.18437 0.0018813  1.1532 0.15291    
## Dat$Column:Dat$Row             106 0.26715 0.0025203  1.5448 0.00052 ***
## Dat$Height:Dat$Column:Dat$Row  212 0.41022 0.0019350  1.1861 0.04567 *  
## Residuals                     1313 2.14207 0.0016314                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.2917691

## Analysis of Variance Table
## 
## Response: Dat$Chl5
##                                 Df Sum Sq Mean Sq F value   Pr(>F)   
## Dat$Height                       2    791  395.31  1.8944 0.150825   
## Dat$Column                       3    197   65.56  0.3142 0.815149   
## Dat$Row                         49    156    3.19  0.0153 1.000000   
## Dat$Height:Dat$Column            6   1277  212.78  1.0196 0.410662   
## Dat$Height:Dat$Row              98  26884  274.33  1.3146 0.024850 * 
## Dat$Column:Dat$Row             106  23892  225.40  1.0801 0.279036   
## Dat$Height:Dat$Column:Dat$Row  212  59481  280.57  1.3445 0.001572 **
## Residuals                     1313 273994  208.68                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.2914021

## Analysis of Variance Table
## 
## Response: Dat$Flav5
##                                 Df Sum Sq  Mean Sq F value    Pr(>F)    
## Dat$Height                       2  0.001 0.000562  0.0115  0.988538    
## Dat$Column                       3  0.011 0.003615  0.0742  0.973871    
## Dat$Row                         49  0.214 0.004364  0.0895  1.000000    
## Dat$Height:Dat$Column            6  1.000 0.166608  3.4182  0.002368 ** 
## Dat$Height:Dat$Row              98  5.708 0.058249  1.1951  0.100948    
## Dat$Column:Dat$Row             106 14.742 0.139078  2.8534 < 2.2e-16 ***
## Dat$Height:Dat$Column:Dat$Row  212 12.986 0.061253  1.2567  0.011685 *  
## Residuals                     1313 63.997 0.048741                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.3513279

## Analysis of Variance Table
## 
## Response: Dat$NBI5
##                                 Df Sum Sq Mean Sq F value   Pr(>F)    
## Dat$Height                       2      3   1.352  0.0212 0.978979    
## Dat$Column                       3     50  16.825  0.2644 0.851104    
## Dat$Row                         49     22   0.453  0.0071 1.000000    
## Dat$Height:Dat$Column            6    795 132.483  2.0816 0.052659 .  
## Dat$Height:Dat$Row              98   8157  83.239  1.3079 0.027100 *  
## Dat$Column:Dat$Row             106   7730  72.925  1.1458 0.155856    
## Dat$Height:Dat$Column:Dat$Row  212  18762  88.498  1.3905 0.000485 ***
## Residuals                     1313  83565  63.644                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.2982705

Comparison Heatmaps

Looking at controlled data

Anthocyanin Comparisons

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Chlorophyll Comparisons

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Flavonol Comparisons

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Nitrogen Comparisons

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Correlation Plots Between Chemicals

Comparing correlations between the 4 dualex measures

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Correlation ANOVAs

## Analysis of Variance Table
## 
## Response: Anth5
##                   Df  Sum Sq Mean Sq  F value    Pr(>F)    
## Chl5               1 0.32495 0.32495 225.0996 < 2.2e-16 ***
## Flav5              1 0.06820 0.06820  47.2416 8.648e-12 ***
## NBI5               1 0.02278 0.02278  15.7770 7.410e-05 ***
## Chl5:Flav5         1 0.00951 0.00951   6.5852   0.01036 *  
## Chl5:NBI5          1 0.02430 0.02430  16.8357 4.260e-05 ***
## Flav5:NBI5         1 0.00218 0.00218   1.5087   0.21950    
## Chl5:Flav5:NBI5    1 0.00016 0.00016   0.1093   0.74100    
## Residuals       1782 2.57247 0.00144                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.1494671

## Analysis of Variance Table
## 
## Response: Chl5
##                    Df Sum Sq Mean Sq    F value Pr(>F)    
## Anth5               1  41543   41543 2.5419e+04 <2e-16 ***
## Flav5               1    202     202 1.2366e+02 <2e-16 ***
## NBI5                1 335250  335250 2.0513e+05 <2e-16 ***
## Anth5:Flav5         1    494     494 3.0218e+02 <2e-16 ***
## Anth5:NBI5          1    167     167 1.0217e+02 <2e-16 ***
## Flav5:NBI5          1   6102    6102 3.7336e+03 <2e-16 ***
## Anth5:Flav5:NBI5    1      0       0 1.5590e-01  0.693    
## Residuals        1782   2912       2                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.9924682

## Analysis of Variance Table
## 
## Response: Flav5
##                   Df Sum Sq Mean Sq   F value    Pr(>F)    
## Anth5              1  3.055   3.055  265.2320 < 2.2e-16 ***
## Chl5               1  0.056   0.056    4.8612   0.02759 *  
## NBI5               1 74.121  74.121 6435.9193 < 2.2e-16 ***
## Anth5:Chl5         1  0.522   0.522   45.3622 2.203e-11 ***
## Anth5:NBI5         1  0.178   0.178   15.4884 8.620e-05 ***
## Chl5:NBI5          1  0.204   0.204   17.7366 2.664e-05 ***
## Anth5:Chl5:NBI5    1  0.000   0.000    0.0127   0.91035    
## Residuals       1782 20.523   0.012                        
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.7919831

Nitrogen Balance Index Anova

## Analysis of Variance Table
## 
## Response: NBI5
##                    Df Sum Sq Mean Sq    F value Pr(>F)    
## Anth5               1  13748   13748 2.0883e+04 <2e-16 ***
## Chl5                1  93493   93493 1.4201e+05 <2e-16 ***
## Flav5               1   9187    9187 1.3954e+04 <2e-16 ***
## Anth5:Chl5          1     49      49 7.4257e+01 <2e-16 ***
## Anth5:Flav5         1    103     103 1.5674e+02 <2e-16 ***
## Chl5:Flav5          1   1331    1331 2.0210e+03 <2e-16 ***
## Anth5:Chl5:Flav5    1      0       0 2.4100e-02 0.8768    
## Residuals        1782   1173       1                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r squared

## [1] 0.990148

Flowering Status and Chemical Measures

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Flowering ANOVAs

## Analysis of Variance Table
## 
## Response: Dat$Anth5
##              Df  Sum Sq    Mean Sq F value Pr(>F)
## Dat$Flower    1 0.00036 0.00036392  0.2152 0.6428
## Residuals  1788 3.02417 0.00169137

## [1] 0.0001203217

## Analysis of Variance Table
## 
## Response: Dat$Chl5
##              Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower    1     19  18.847  0.0872 0.7679
## Residuals  1788 386652 216.248

## [1] 4.87422e-05

## Analysis of Variance Table
## 
## Response: Dat$Flav5
##              Df Sum Sq  Mean Sq F value  Pr(>F)  
## Dat$Flower    1  0.245 0.244694  4.4456 0.03513 *
## Residuals  1788 98.414 0.055042                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.002480194

## Analysis of Variance Table
## 
## Response: Dat$NBI5
##              Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower    1     13  13.012  0.1954 0.6585
## Residuals  1788 119071  66.595

## [1] 0.0001092696

Preparing Data for Environement and Height Analysis

## Warning: NAs introduced by coercion

Environment and Height 2018

Environmental AVONA 2018

## Analysis of Variance Table
## 
## Response: H089$Anth
##                Df   Sum Sq    Mean Sq F value Pr(>F)  
## H089$Height08   1 0.001142 0.00114216  4.1262  0.044 *
## Residuals     149 0.041244 0.00027681                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.02694634

## Analysis of Variance Table
## 
## Response: H089$Chl
##                Df Sum Sq Mean Sq F value   Pr(>F)   
## H089$Height08   1  201.5 201.535  7.7025 0.006222 **
## Residuals     149 3898.6  26.165                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.04915359

## Analysis of Variance Table
## 
## Response: H089$Flav
##                Df  Sum Sq   Mean Sq F value Pr(>F)
## H089$Height08   1 0.01095 0.0109540  1.2597 0.2635
## Residuals     149 1.29569 0.0086959

## [1] 0.008383348

## Analysis of Variance Table
## 
## Response: H089$NBI
##                Df  Sum Sq Mean Sq F value  Pr(>F)  
## H089$Height08   1   29.09 29.0855   3.224 0.07459 .
## Residuals     149 1344.22  9.0216                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.0211791

Environment and Height 2019

Environmental AVONA 2019

## Analysis of Variance Table
## 
## Response: H089$Anth
##                Df   Sum Sq    Mean Sq F value  Pr(>F)  
## H089$Height09   1 0.001706 0.00170619  6.2493 0.01351 *
## Residuals     149 0.040680 0.00027302                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.0402532

## Analysis of Variance Table
## 
## Response: H089$Chl
##                Df Sum Sq Mean Sq F value  Pr(>F)  
## H089$Height09   1  177.8 177.810  6.7547 0.01029 *
## Residuals     149 3922.3  26.324                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.0433673

## Analysis of Variance Table
## 
## Response: H089$Flav
##                Df  Sum Sq   Mean Sq F value Pr(>F)
## H089$Height09   1 0.00096 0.0009574  0.1093 0.7415
## Residuals     149 1.30568 0.0087630

## [1] 0.0007327395

## Analysis of Variance Table
## 
## Response: H089$NBI
##                Df  Sum Sq Mean Sq F value  Pr(>F)  
## H089$Height09   1   41.76  41.757  4.6726 0.03224 *
## Residuals     149 1331.55   8.937                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.03040622

Environment and Height Differences 2018/2019

Height Differences 2018/2019 ANOVAs

## Analysis of Variance Table
## 
## Response: H089$Anth
##            Df   Sum Sq    Mean Sq F value Pr(>F)
## H089$Diff   1 0.000292 0.00029205  1.0337 0.3109
## Residuals 149 0.042094 0.00028251

## [1] 0.006890089

## Analysis of Variance Table
## 
## Response: H089$Chl
##            Df Sum Sq Mean Sq F value Pr(>F)
## H089$Diff   1    0.9  0.9142  0.0332 0.8556
## Residuals 149 4099.2 27.5113

## [1] 0.0002229737

## Analysis of Variance Table
## 
## Response: H089$Flav
##            Df  Sum Sq  Mean Sq F value  Pr(>F)  
## H089$Diff   1 0.03732 0.037322  4.3811 0.03803 *
## Residuals 149 1.26932 0.008519                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.02856334

## Analysis of Variance Table
## 
## Response: H089$NBI
##            Df  Sum Sq Mean Sq F value Pr(>F)
## H089$Diff   1    6.38  6.3824  0.6957 0.4056
## Residuals 149 1366.93  9.1740

## [1] 0.004647459

Height Distribution Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Row Height Plots

Row Height ANOVAs

## Analysis of Variance Table
## 
## Response: H0892$Height08
##            Df Sum Sq Mean Sq F value   Pr(>F)   
## H0892$Row  49  48775  995.40  1.9908 0.001269 **
## Residuals 121  60501  500.01                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.446346

## Analysis of Variance Table
## 
## Response: H0892$Height09
##            Df Sum Sq Mean Sq F value    Pr(>F)    
## H0892$Row  49  62417 1273.82  2.0535 0.0007953 ***
## Residuals 121  75058  620.32                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.4540239

## Analysis of Variance Table
## 
## Response: H0892$Diff
##            Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Row  49  12742  260.04  0.7015 0.9197
## Residuals 121  44853  370.69

## [1] 0.221231

Column Height Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Column Height ANOVAs

## Analysis of Variance Table
## 
## Response: H0892$Height08
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## H0892$Column   3  30712 10237.3  21.761 6.033e-12 ***
## Residuals    167  78564   470.4                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.281048

## Analysis of Variance Table
## 
## Response: H0892$Height09
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## H0892$Column   3  49883 16627.8  31.702 2.842e-16 ***
## Residuals    167  87592   524.5                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.3628519

## Analysis of Variance Table
## 
## Response: H0892$Diff
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## H0892$Column   3   3480 1159.90  3.5794 0.01519 *
## Residuals    167  54115  324.04                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.06041647

Adjusting Height by Environment

H089C = group_by(H0892, Column)
H089C = summarise(H089C, ColHM8 = mean(Height08, na.rm = T), ColHM9 = mean(Height09, na.rm = T), CDiff = mean(Diff, na.rm = T))

HData = merge(H0892 ,H089C, by.x = "Column", all = TRUE)
HData = merge(HData ,H089R, by.x = "Row", all = TRUE)

HData$HM8 = mean(HData$Height08)
HData$HM9 = mean(HData$Height09)

HData$CD8 = HData$ColHM8 - HData$HM8
HData$CD9 = HData$ColHM9 - HData$HM9

HData$RD8 = HData$RowHM8 - HData$HM8
HData$RD9 = HData$RowHM9 - HData$HM9

HData$F8 = HData$Height08 - HData$HM8 - HData$CD8 - HData$RD8
HData$F9 = HData$Height09 - HData$HM9 - HData$CD9 - HData$RD9

HData$FDiff = HData$F9 - HData$F8
#write.xlsx(HData, "HData.xlsx")

Adjusted 2018 Height Map

Adjusted 2019 Height Map

Adjusted 2018-19 Height Difference Map

Adjusted Height Distribution Histograms

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Adjusted Row Height Plots

Adjusted Row ANOVAs

## Analysis of Variance Table
## 
## Response: H0892$Height08
##            Df Sum Sq Mean Sq F value   Pr(>F)   
## H0892$Row  49  48775  995.40  1.9908 0.001269 **
## Residuals 121  60501  500.01                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.446346

## Analysis of Variance Table
## 
## Response: H0892$Height09
##            Df Sum Sq Mean Sq F value    Pr(>F)    
## H0892$Row  49  62417 1273.82  2.0535 0.0007953 ***
## Residuals 121  75058  620.32                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.4540239

## Analysis of Variance Table
## 
## Response: H0892$Diff
##            Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Row  49  12742  260.04  0.7015 0.9197
## Residuals 121  44853  370.69

## [1] 0.221231

Adjusted Column Height Plots

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

Adjusted Column ANOVAs

## Analysis of Variance Table
## 
## Response: HData$F8
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## HData$Column   3   2427  809.10   3.181 0.02546 *
## Residuals    167  42477  254.35                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.05405508

## Analysis of Variance Table
## 
## Response: HData$F9
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## HData$Column   3   3731 1243.57  4.7497 0.003321 **
## Residuals    167  43724  261.82                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] 0.07861582

## Analysis of Variance Table
## 
## Response: HData$FDiff
##               Df Sum Sq Mean Sq F value Pr(>F)
## HData$Column   3    150  50.054  0.1985 0.8973
## Residuals    167  42111 252.161

## [1] 0.003553225

Corrlelations Between 2018 Heights and Leaf Chemistry

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

2018 Heights and Leaf Chemistry ANOVAs

## Analysis of Variance Table
## 
## Response: HData$F8
##             Df Sum Sq Mean Sq F value Pr(>F)
## HData$Anth   1     18  17.905    0.07 0.7917
## Residuals  149  38117 255.816

## [1] 0.0004695246

## Analysis of Variance Table
## 
## Response: HData$F8
##            Df Sum Sq Mean Sq F value Pr(>F)
## HData$Chl   1     35   35.29   0.138 0.7108
## Residuals 149  38099  255.70

## [1] 0.0009254087

## Analysis of Variance Table
## 
## Response: HData$F8
##             Df Sum Sq Mean Sq F value Pr(>F)
## HData$Flav   1    270  270.08  1.0628 0.3043
## Residuals  149  37864  254.12

## [1] 0.007082224

## Analysis of Variance Table
## 
## Response: HData$F8
##            Df Sum Sq Mean Sq F value Pr(>F)
## HData$NBI   1     63   62.65  0.2452 0.6212
## Residuals 149  38072  255.51

## [1] 0.00164288

Corrlelations Between 2019 Heights and Leaf Chemistry

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

2019 Heights and Leaf Chemistry ANOVAs

## Analysis of Variance Table
## 
## Response: HData$F9
##             Df Sum Sq Mean Sq F value Pr(>F)
## HData$Anth   1    219  218.56  0.8352 0.3622
## Residuals  149  38991  261.69

## [1] 0.00557422

## Analysis of Variance Table
## 
## Response: HData$F9
##            Df Sum Sq Mean Sq F value Pr(>F)
## HData$Chl   1     74  73.784  0.2809 0.5969
## Residuals 149  39136 262.656

## [1] 0.001881796

## Analysis of Variance Table
## 
## Response: HData$F9
##             Df Sum Sq Mean Sq F value Pr(>F)
## HData$Flav   1    489  488.64  1.8803 0.1724
## Residuals  149  38721  259.87

## [1] 0.01246224

## Analysis of Variance Table
## 
## Response: HData$F9
##            Df Sum Sq Mean Sq F value Pr(>F)
## HData$NBI   1     82  81.895  0.3119 0.5774
## Residuals 149  39128 262.602

## [1] 0.00208866

Corrlelations Between Height Differences 2018-2019 and Leaf Chemistry

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 20 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_point).

Height Differences 2018-2019 and Leaf Chemistry ANOVAs

## Analysis of Variance Table
## 
## Response: HData$FDiff
##             Df Sum Sq Mean Sq F value Pr(>F)
## HData$Anth   1    111  111.35  0.4623 0.4976
## Residuals  149  35888  240.86

## Analysis of Variance Table
## 
## Response: HData$FDiff
##            Df Sum Sq Mean Sq F value Pr(>F)
## HData$Chl   1    211  211.13   0.879   0.35
## Residuals 149  35788  240.19

## Analysis of Variance Table
## 
## Response: HData$FDiff
##             Df Sum Sq Mean Sq F value  Pr(>F)  
## HData$Flav   1   1485 1485.27   6.412 0.01237 *
## Residuals  149  34514  231.64                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: HData$FDiff
##            Df Sum Sq Mean Sq F value Pr(>F)
## HData$NBI   1      1   1.287  0.0053 0.9419
## Residuals 149  35998 241.599

Height and Flowering

##          Min.    1st Qu.     Median      Mean  3rd Qu.     Max.
## FF8 -36.70946  -9.485302 -0.2189327 1.1934569 13.09803 39.74833
## FF9 -39.30024  -7.532247  2.8441640 1.3495701 10.20662 39.40241
## FFD -58.17411 -11.058077  2.0958348 0.1561132  9.57589 42.32589

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Warning: `fun.y` is deprecated. Use `fun` instead.

## Analysis of Variance Table
## 
## Response: Flower$F8
##                Df Sum Sq Mean Sq F value Pr(>F)
## Flower$Flower   1    511   511.4  2.0253 0.1568
## Residuals     149  37623   252.5

## [1] 0.01341057

## Analysis of Variance Table
## 
## Response: Flower$F9
##                Df Sum Sq Mean Sq F value Pr(>F)
## Flower$Flower   1      3   2.845  0.0108 0.9173
## Residuals     149  39207 263.132

## [1] 7.255893e-05

## Analysis of Variance Table
## 
## Response: Flower$FDiff
##                Df Sum Sq Mean Sq F value Pr(>F)
## Flower$Flower   1    591  590.54   2.485 0.1171
## Residuals     149  35409  237.64

## [1] 0.01640405

ManukaAnalysis

Melissa Taane

06/05/2019

F1 Layout

Height Diagram

Import and Arrange Data

Replicate Analysis

Replicate Data Overview

Replicate Group means

Anthocyanin Replicate Plots

Chlorphyll Replicate Plots

Flavonol Replicate Plots

Nitrogen Balance Replicate Plots

ANOVA of Replicates

Allocation Analysis

Allocation Data Overview

Allocation Group Means

Boxplots Comparing Mean, Median and Measurement Distributions of the Allocation Groups

Allocation ANOVAs

Parent Tree Analysis

Parent Tree Data Overview

Parent Tree Means

Anthocyanin Parent Plots

Chlorophyll Parent Plots

Flavonol Parent Plots

Nitrogen Parent Plots

Parent t.tests

Parental Cross Analysis (F1 Generation)

A Brief Look at Some Tree Data

Anthocyanin Overview

Chlorophyll Overview

Flavonol Overview

Nitrogen Overview

A Closer Look at Invidual F1 Trees and Their Measurements

IN4G5

IN4EP

IN4EY

IN4CD

IN4D8

IN4GV

IN4C2

IN4E3

IN4HK

IN4GC

F1 Height Analysis

Height Measure Distributions

Anthocyanin

Chlorophyll

Flavonol

Nitrogen

ANOVAS

Height Analysis within Trees

Anthocyanin High vs Low

Anthocyanin High vs Mid

Anthocyanin Mid vs Low

Chlorophyll High vs Low

Chlorophyll High vs Mid

Chlorophyll Mid vs Low

Flavonol High vs Low

Flavonol High vs Mid

Flavonol Mid vs Low

NBI High vs Low

NBI High vs Mid

NBI Mid vs Low

Row Analysis

Sample of Row Means

Investigating Differences Between Rows with 2,3 and 4 Trees.

Summary of Rows with 2 Trees

Summary of Rows with 3 Trees

Summary of Rows with 4 Trees

Looking at Row Differences in Variance

T.Tests to Compare Similarity Between Rows with 3 and Rows with 4 Trees.

Row Plots and ANOVAs

Anthocyanin

Chlorophyll

Flavonol

Nitrogen

Row ANOVA

Column Analysis

Column Means